Kullback-Leibler Divergence (KL Divergence) is a measure used in statistics and information theory to quantify how one probability distribution diverges from a second, expected probability distribution. It indicates the amount of information lost when approximating one distribution with another.
Applications/Use Cases:
- Machine Learning: Evaluating how well a model’s predicted probability distribution matches the true distribution of data.
- Data Compression: Assessing the efficiency of data encoding schemes.
- Anomaly Detection: Identifying unusual patterns by comparing observed data distributions to expected ones.